DSpace/Manakin Repository

In the social and behavioral sciences, a general interest exists in the comparison of development between groups, especially when one of the groups is exceptional and abnormal development is expected. Multiple group latent growth models enable these comparisons. However, the combination of a smaller subgroup with a larger reference group ... read more has been shown to cause issues with power and Type I errors. The current study explores the limits of subsample sizes in latent growth modeling (LGM) that can and cannot be analyzed with Maximum Likelihood and Bayesian estimation, where Bayesian estimation was examined not only with uninformed, but also with informed priors. The results show that Bayesian estimation resolves computational issues that occur with ML estimation, and that the addition of prior information can be the key to achieving sufficient power to detect a small growth difference between groups. Prior information has to be acquired, especially with respect to the exceptional group, to promote statistical power. The study continues with an empirical example that shows how the simulation results can be implemented, and how prior information can be acquired systematically to serve as input for prior distributions. In addition, a decision tree and general recommendations are provided to guide applied researchers. show less

Download/Full Text

Not available. The author may have various reasons for not providing access, for instance because it is prohibited by the commissioner of the research, or because the author is conducting further research on the subject.